{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df3b00a9",
   "metadata": {},
   "source": [
    "## Interchange Intervention"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89f31a38",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/stanfordnlp/pyvene/blob/main/tutorials/basic_tutorials/Basic_Intervention.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d4afb217",
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = \"Aryaman Arora and Zhengxuan Wu\"\n",
    "__version__ = \"10/05/2023\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1488cea4",
   "metadata": {},
   "source": [
    "### Overview\n",
    "\n",
    "This tutorial aims to reproduce some of the results in this [notebook](https://github.com/aryamanarora/nano-causal-interventions/blob/main/The%20capital%20of%20France%20is.ipynb) for path patching or causal scrubbing. This library could potentially support other kinds of interventions that were not originally supported by previous works."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f10fade",
   "metadata": {},
   "source": [
    "### Set-up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "474750ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024-01-11 01:18:09,028] [INFO] [real_accelerator.py:158:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    # This library is our indicator that the required installs\n",
    "    # need to be done.\n",
    "    import pyvene\n",
    "\n",
    "except ModuleNotFoundError:\n",
    "    !pip install git+https://github.com/stanfordnlp/pyvene.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9c684415",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pyvene\n",
    "from pyvene import embed_to_distrib, top_vals, format_token\n",
    "from pyvene import RepresentationConfig, IntervenableConfig, IntervenableModel\n",
    "from pyvene import VanillaIntervention\n",
    "\n",
    "%config InlineBackend.figure_formats = ['svg']\n",
    "from plotnine import (\n",
    "    ggplot,\n",
    "    geom_tile,\n",
    "    aes,\n",
    "    facet_wrap,\n",
    "    theme,\n",
    "    element_text,\n",
    "    geom_bar,\n",
    "    geom_hline,\n",
    "    scale_y_log10,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaf70de7",
   "metadata": {},
   "source": [
    "### Factual recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56cc896c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded model\n",
      "The capital of Spain is\n",
      "_Madrid              0.10501234978437424\n",
      "_the                 0.0949699655175209\n",
      "_Barcelona           0.0702790841460228\n",
      "_a                   0.04010068252682686\n",
      "_now                 0.02824278175830841\n",
      "_in                  0.02759990654885769\n",
      "_Spain               0.022991720587015152\n",
      "_Catalonia           0.018823225051164627\n",
      "_also                0.018689140677452087\n",
      "_not                 0.01735665090382099\n",
      "\n",
      "The capital of Italy is\n",
      "_Rome                0.15734916925430298\n",
      "_the                 0.07316355407238007\n",
      "_Milan               0.046878915280103683\n",
      "_a                   0.03449810668826103\n",
      "_now                 0.03200329467654228\n",
      "_in                  0.02306535840034485\n",
      "_also                0.02274816483259201\n",
      "_home                0.01920313946902752\n",
      "_not                 0.01640527881681919\n",
      "_Italy               0.01577090471982956\n"
     ]
    }
   ],
   "source": [
    "config, tokenizer, gpt = pyvene.create_gpt2()\n",
    "\n",
    "base = \"The capital of Spain is\"\n",
    "source = \"The capital of Italy is\"\n",
    "inputs = [tokenizer(base, return_tensors=\"pt\"), tokenizer(source, return_tensors=\"pt\")]\n",
    "print(base)\n",
    "res = gpt(**inputs[0])\n",
    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)\n",
    "print()\n",
    "print(source)\n",
    "res = gpt(**inputs[1])\n",
    "distrib = embed_to_distrib(gpt, res.last_hidden_state, logits=False)\n",
    "top_vals(tokenizer, distrib[0][-1], n=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d532a8e8",
   "metadata": {},
   "source": [
    "### Patch Patching on Position-aligned Tokens\n",
    "We path patch on two modules on each layer:\n",
    "- [1] MLP output (the MLP output will be from another example)\n",
    "- [2] MHA input (the self-attention module input will be from another module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73c4ade3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_position_config(model_type, component, layer):\n",
    "    config = IntervenableConfig(\n",
    "        model_type=model_type,\n",
    "        representations=[\n",
    "            RepresentationConfig(\n",
    "                layer,              # layer\n",
    "                component,          # component\n",
    "                \"pos\",              # intervention unit\n",
    "                1,                  # max number of unit\n",
    "            ),\n",
    "        ],\n",
    "        intervention_types=VanillaIntervention,\n",
    "    )\n",
    "    return config\n",
    "\n",
    "\n",
    "base = tokenizer(\"The capital of Spain is\", return_tensors=\"pt\")\n",
    "sources = [tokenizer(\"The capital of Italy is\", return_tensors=\"pt\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3cc24ea1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# should finish within 1 min with a standard 12G GPU\n",
    "tokens = tokenizer.encode(\" Madrid Rome\")\n",
    "\n",
    "data = []\n",
    "for layer_i in range(gpt.config.n_layer):\n",
    "    config = simple_position_config(type(gpt), \"mlp_output\", layer_i)\n",
    "    intervenable = IntervenableModel(config, gpt)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base, sources, {\"sources->base\": pos_i}\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"f{layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"mlp_output\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "    config = simple_position_config(type(gpt), \"attention_input\", layer_i)\n",
    "    intervenable = IntervenableModel(config, gpt)\n",
    "    for pos_i in range(len(base.input_ids[0])):\n",
    "        _, counterfactual_outputs = intervenable(\n",
    "            base, sources, {\"sources->base\": pos_i}\n",
    "        )\n",
    "        distrib = embed_to_distrib(\n",
    "            gpt, counterfactual_outputs.last_hidden_state, logits=False\n",
    "        )\n",
    "        for token in tokens:\n",
    "            data.append(\n",
    "                {\n",
    "                    \"token\": format_token(tokenizer, token),\n",
    "                    \"prob\": float(distrib[0][-1][token]),\n",
    "                    \"layer\": f\"a{layer_i}\",\n",
    "                    \"pos\": pos_i,\n",
    "                    \"type\": \"attention_input\",\n",
    "                }\n",
    "            )\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1cfab3b",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "df[\"layer\"] = df[\"layer\"].astype(\"category\")\n",
    "df[\"token\"] = df[\"token\"].astype(\"category\")\n",
    "nodes = []\n",
    "for l in range(gpt.config.n_layer - 1, -1, -1):\n",
    "    nodes.append(f\"f{l}\")\n",
    "    nodes.append(f\"a{l}\")\n",
    "df[\"layer\"] = pd.Categorical(df[\"layer\"], categories=nodes[::-1], ordered=True)\n",
    "\n",
    "g = (\n",
    "    ggplot(df)\n",
    "    + geom_tile(aes(x=\"pos\", y=\"layer\", fill=\"prob\", color=\"prob\"))\n",
    "    + facet_wrap(\"~token\")\n",
    "    + theme(axis_text_x=element_text(rotation=90))\n",
    ")\n",
    "print(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3a191190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "filtered = df\n",
    "filtered = filtered[filtered[\"pos\"] == 4]\n",
    "g = (\n",
    "    ggplot(filtered)\n",
    "    + geom_bar(aes(x=\"layer\", y=\"prob\", fill=\"token\"), stat=\"identity\")\n",
    "    + theme(axis_text_x=element_text(rotation=90), legend_position=\"none\")\n",
    "    + scale_y_log10()\n",
    "    + facet_wrap(\"~token\", ncol=1)\n",
    ")\n",
    "print(g)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
